What’s New in OBIEE On-Prem 12.2.1.4

Join us on June 19, 2018 at 12:00pm Central for a webinar on new features in OBIEE On-Prem 12.2.1.4! Registration for this webinar is now open.

While much of the news on Oracle Analytics has been about capabilities in the Oracle Analytics Cloud (OAC) service, Oracle Business Intelligence Enterprise Edition (OBIEE) has gained many new features in version 12.2.1.4, released in the past month or so. In this release are many features in the Data Visualization interface that have made their way into various releases of OAC, but are new to the on-premise release of OBIEE.

Join Dan Vlamis, Oracle ACE Director, as he shows these features in a live webcast and contrasts the differences between OAC and OBIEE. Expect a fast-paced presentation with live demos of OBIEE (and OAC!) on a new SampleApp image, on as many of the new features as time allows including:

  • Connect to More Databases
  • Data Flow Enhancements
  • Improved Narration and Storytelling Features
  • Include Links to Related Content in Your Project
  • Numeric Values in File-Based Data Sources Uploaded as Measures
  • More Display Formatting Options for Numbers and Dates
  • New Properties Area in the Data Panel
  • Improved Sharing
  • More Options to Copy, Paste, and Duplicate
  • Add Unrelated Data Sets to the Same Project
  • Date and Time Intelligence
  • Data Warning Indicator
  • Background Maps
  • Coloring Maps Using Attribute Column Values

Just like our previous demonstrations of new BI releases (see www.vlamis.com/obiwebinars for a list), we will give a live demonstration of as many of these features as we have time for.

Join us at Kscope 18!

Join Arthur Dayton and Tim Vlamis in Orlando this June for the ODTUG Kscope 18 conference. Together they are sharing four presentations:

In addition, Tim Vlamis will present with Mike Durran of Oracle:

Let us know if you are also attending Kscope this year as we would love to set aside some time to speak with you!

For more information on these presentations, visit our Presentations Page.

Machine Learning and You: K-Means Clustering

We are seeing a tremendous amount of interest in people wanting to learn more about machine learning, AI, data mining, and predictive analytics. Our presentations addressing these topics are often some of the best attended at conferences. I believe there are two fundamental drivers for this: the first is a sincere desire to learn about new things and new capabilities. The second is a very real fear of not knowing about machine learning and the like and having others (particularly higher-level executives) expect that you know about it. We see this especially with people involved in business intelligence and analytics on the business side and also with IT managers and executives who are expected to explain what their strategy or road map is for machine learning and advanced analytics. However, the observation that machine learning is a lot like teenage sex is fairly apt: everyone talks about it but few actually do it. And many of those who do do it, don’t really know what they are doing.

Here are some of things that we try to emphasize to get people over their fear.

  • These are just calculations like thousands of other calculations in your system.
  • You typically don’t get only one answer, but a series of answers that can be considered together to provide context on how to use them.
  • You don’t have to know the specifics of the calculation to be able to understand and use the results.
  • You likely understand the concepts, but the words that are used are unfamiliar and sound hard. Once you learn some new vocabulary, it seems like a normal part of your system.
  • It’s helpful to be able to explain the basics to others. This skill frees analysts to use the powerful and accessible machine learning tools in the Oracle Analytics Cloud. No one likes getting “challenge questions” from their superiors and being unable to answer them.

For example, let’s look at a K-Means clustering algorithm. Clustering is used to help reveal similarities and differences in sets of data elements such as customers, orders, locations, products, etc. Whenever you have a lot of something and you want to know “what are the natural patterns in similarities and differences and how can we organize this very large set into smaller sets of like elements that are different from one another?”, we use clustering.

A very common business use case is to cluster the set of customers that a business has into segments that are differentiated from one another. We do this all the time with business intelligence queries. We separate customers and aggregate the results by all kinds of attributes. We might look at customers by location and separate them by state or zip code and then look at the aggregated results to see where customers are buying the most and the least. We might look at customers by what products or services they purchased or when they made their purchases. We might use descriptive attributes such as number of employees, or how long they’ve been a customer, or whether they purchase online or in-store. What is common for all of these analyses is that the analyst is determining how to organize the groups of customers. We might have dozens or even hundreds of possible attributes which we could use.

Clustering offers us a methodology for letting the data tell us where the commonalities exist across many dimensions and many attributes. There is no right and wrong in clustering. Just a mathematical calculation and cluster assignment. But there is also a lot of other information we can get about both the clusters and about the members of each cluster. How many members are in each cluster? What is the average or center of each cluster? How close or far apart are those cluster centers from each other? How close is each member of a cluster to its center? Which of the attributes contributes the most in determining cluster assignments?

The K-Means clustering algorithm is just a method for organizing the elements by which ones are close together. It is a procedure for minimizing the total distance between a given number of centroids (cluster centers) and the data elements. To be fair, there are several different versions of K-Means, but fundamentally, it is a distance minimization algorithm. So all we’re doing is grouping customers into a given number of segments and using math and the data itself to make the assignments rather than making the assignments using a human-stated rule(s).

If you wondering, well how else could we determine which element goes to which cluster besides distance? There are two other basic methods, drawing straight lines through the data space to separate elements into areas of similarity and calculating the density of areas to determine regions of similarity. Sounds easier than Orthogonal Partitioning and Expectation Maximization, right? But more about those algorithms later.

Dan and Tim Vlamis are Presenting at GLOC 2018!

This year the Great Lakes Oracle Conference will take place on May 16th and May 17th at the Cleveland Public Auditorium in Cleveland Ohio. This 2-day conference is packed full of informative and technical sessions suitable for all experience levels.

Dan Vlamis and Tim Vlamis have been accepted to present this year and will be involved in the following sessions:

May 16, 2018

Registration for the Great Lakes Oracle Conference is available by visiting the conference website.

Let us know if you are planning to attend GLOC 18, we would love to meet with you during the conference!

For more information on our presentations visit our website.

Join us at Collaborate 2018!

We are very excited to be attending and speaking at Collaborate 2018! Collaborate takes place from April 22 to 26, 2018 at the Mandalay Bay Resort & Casino in Las Vegas, NV.

This year our presentations include:

We would love to set aside some time to meet with you personally during the conference. Send us an email and let us know what days you are available and we'll reach out to schedule a meeting!

More information can be found at: